This 7-part mini-course will provide a gentle introduction to data science and applied machine learning. If you're a developer, analyst, manager, or aspiring data scientist looking learn more about data science, then you're in the right place.

Chapter 1: Bird's Eye View

First, let’s start with the “80/20” of data science…

Generally speaking, we can break down applied machine learning into the following chunks:

This data science primer will cover exploratory analysis, data cleaning, feature engineering, algorithm selection, and model training. As you can see, those chunks make up 80% of the pie. They also set the foundation for more advanced techniques.

In this first chapter, you’ll see how these moving pieces fit together. Therefore, we suggest the following two tips to making the most out of this primer:

Tip #1 - Don’t sweat the details (for now).

We’ve seen students master this subject 2X faster by first understanding how all the pieces fit together… and then diving deeper. Our trainings all follow this “top-down” approach.

Tip #2 - Don’t worry about coding (yet).

Again, it’s easy to get lost in the weeds at the beginning… so our goal is to see the forest instead of the trees. Don’t worry - We’ll get to the code later.

There’s a lot of trial and error, so how do you avoid chasing dead ends? The answer is “Exploratory Analysis.” (Which is just fancy-talk for “getting to know” your data.)

Doing this upfront helps you save time and avoid wild goose chases… As a data scientist, you are a commander with limited resources (i.e. time).Exploratory analysis is like sending scouts to learn where to deploy your forces!

Chapter 3: Data Cleaning

Proper data cleaning is the “secret” sauce behind machine learning… Well, it’s not really a “secret”… It’s just a bit boring, so no one really talks about it. But the truth is:

Better data beats fancier algorithms…

(Even if you forget everything else from this primer, please remember this point)

Garbage in = Garbage out... Plain and Simple! If you have a clean dataset, even simple algorithms can learn impressive insights from it!

Now, as you might imagine, different problems will require different methods… For now though, let’s at least ensure we know how to fix the most common issues. This chapter will give you a reliable starting point, regardless of your dataset.